Snakes and Ladders Game Code

Hello people…! In this post, we will discuss about the Snakes and Ladders Game Code, where we find the shortest path to win the Snakes and Ladders game by using the Breadth First Search (BFS) Algorithm. If you don’t know the algorithm, I suggest you read my post on Breadth First Search Algorithm.

Now, Graph Theory has many applications and I love working with things that have real-world applications, well, off course the other data structures too have their uses, but the speciality of Graph Theory is its applications have the closest association with our day-to-day activities. And to show you this and to set an example on how the BFS is actually put into action, I am taking up the example of the very popular game, Snakes and Ladders.

This game needs no introduction. We all must have played it in our childhood. I will explain you, how by using Graphs and BFS we can find the Shortest Path to win the game, and, we will state that path and the number of moves of dice it takes too. Have a good look at the Snakes and Ladder board below, we will be using it as an example throughout this post.

Snakes and Ladders

You can see we can reach the finish block by an number of ways, but how do you find out the best…? More importantly, how do you put it as code…? That’s what we are going to do. Now, think of how you can represent the game board in terms of a Graph, by Graph I mean in terms of Vertices and Edges.

Don’t be too hard on yourself… Just take the first 7 blocks and try working out on paper what would be the Edge and what would be the Vertices. If you ponder upon this, it is very easy to tell that the numbered blocks on the game board will be our Vertices, then, what will be the Edges…? This depends on how you can go from one block to another on rolling the dice. For now, forget about the ladders and the snakes, just draw the graph for a small portion of the board. It should look somewhat similar to what is in the picture below –

Dice Roll from Block 1

As you see we would have six ways to leave block 1, depending on the dice roll. Same would be the case for block 2, or, block 3, and so on. Now, there is a very important point to be noted here… Remember, this is Directed Graph…! Because, once you roll 5 and go to block 6, there’s no way to come back. So, the directions are important. Now, let us push up the complexity a bit by adding a ladder to our above sketch in block 6, think what would happen to the edges…?

Dice Roll for Block 1 – 5

If you roll 5 from block 1 you will jump directly to block 27. So is for block 2 when you roll out 4, or, block 3 when you roll out 3 and so on. Now, “logically” speaking, the block 6 does not exists in our graph…! Think about the statement for a while. Whenever you reach block, you are directly jumping to block 27, you don’t stay there. Now, if you were constructing an Adjacency List for this graph…. In the list of adjacent vertices for Vertex 1, would you have Vertex 6 in the list, or Vertex 27…? Vertex 27 of course…! Being at Vertex 6 means being at Vertex 27…!

That is why, our edge arrow did not end at Vertex 6… See it…? One more thing, in your Adjacency List, in the list of adjacent vertices for Vertex 6, what would you have…? Nothing…! Because you cannot come to a situation where you would have to stay on Vertex 6 and roll the dice. So the adjacent nodes list corresponding to Vertex 6 should be empty. These two things are very important, when you implement the Adjacency List for the Snake and Ladder board.

Same would be the case, if a snake was there at a block. Logically that block will not exist as a vertex in our adjacency list, and the corresponding edges must be removed. The only difference being that, the edge created due to a snake can lead you to a block of lower value.

Dice Roll for Block 25

Just to get a better idea of the scenario of a ladder and snake, I have depicted what the Adjacency List would look like for the above two examples shown in the pictures.

Adjustments to the Adjacency List

That pic should really clarify all the confusion about the graph. If you still don’t get it, feel free to comment your query…! Now, what do we do once we have the Adjacency List ready…? We just call the Breadth First Search method on that list…! Wait… Really…?! That’s it…? Yes…!

You see the hardest part here in solving the Snakes and Ladder by graphs is correctly determining what your Vertices and Edges are. Once you get that, all you have to do is the Breadth First Search in the resultant graph. Then you can get the shortest path from Vertex 1 to Vertex 100. Now, try getting the Adjacency Lit correct and simply call the BFS method. I’m sure you will succeed if you put in a little dedication. But for those who tried and failed. I have put my code below. Before you look at my code, there are a few logics I have used –

In the beginning of the program, I added all the edges as though the Game Board had no snakes or ladders at all (these number of edges is what I printed), then, I removed the respective edges concerning the snakes and ladders one-by-one.

When a Vertex ‘n’ has a ladder or a snake, we are supposed to replace the corresponding edges as I depicted in the pictures above, for that, I replaced the Vertex n‘s edge with the new value, in Vertices (n – 1), (n – 2), (n – 3), (n – 4), (n – 5), (n – 6). Because it is only in these vertices that you can find an edge of vertex n.

I put all this replacing stuff in a function replace() which takes the Linked List and searches for a value ‘oldVertex’ and replaces it with the value ‘newVertex’ when it finds it.

I used an extra element in my array to make them 1 – index based.

The number of moves to complete the shortest path would be the level of the last vertex, Vertex 100. Why…?! Think about it for a minute and you’ll get it…!

I have added a recursive function, printShortestPath() which recursively looks at the parent of each vertex until the start vertex is reached. It keeps printing vertices as the recursion stack keeps popping out, thus we get the path in a reverse order. Think about this for a while… Trust me… It is easy…! 😉

If you can solve this problem, then you can solve Snakes and Ladders: The Quickest Way Up problem of HackerRank. You can think of extending this to a 20 ✗ 20 Snakes and Ladder board or an n ✗ n board. You just have to add n2 edges to the graph. Solving puzzles by Graph Theory is real fun. I hope this post helped you. If you have any doubts, feel free to comment them. Keep practicing… and… Happy Coding…! 🙂

The “Dice X” notation labels the edges… It denotes if ‘X’ appears on the dice, where would you go from the current position to the next position… For example… Vertex 1 → Dice 1 → Vertex 2… This means when you get 1 on your dice, you move from Vertex 1 to Vertex 2… So, in my figure where there’s a ladder… Let’s say you are on Vertex 1, by Dice 1 (arrow given below) you go to Vertex 2, then by Dice 4 (arrow on top) you go to Vertex 6, which is being on Vertex 27… So the path would be…. Vertex 1 → Dice 1 → Vertex 2 → Dice 4 → Vertex 6 (→ ladder →) Vertex 27 ….. It is simple… Just give it a little thought! 😉

hey man, I’m unable to understand what’s happening in breadthFirstSearch function, to be more specific its about the parent and level arrays and also in this function you have commented a working portion of the code.

Sorry for the late reply… We have applied BFS on all the vertices… BFS gives us the shortest distances from a vertex to the start vertex, which is nothing but the level of that vertex, and we also get the parent of each vertex, that is, from which vertex have we reached a particular vertex on our shortest path. Now, I am accessing level[100], where the value 100 is stored in vertices… So I get the shortest path to the 100th vertex… To print the path, I recursively keep looking at the parent of the vertices starting from 100th vertex till I’ve reached the start vertex… I hope you get it, give it some time, I’m sure you’ll understand it! 🙂

I haven’t done any projects based on Graph theory yet, but I guess they’ll mostly include visualization related ones…. Like, building a maze, or a racetrack (maybe done by randomized DFS)… Or you could do graph coloring related projects like coloring world map, or the traffic flow problem… Or you could do some visualization of routing algorithms… Looking to do a project to build a maze… I’ll surely make a post here if I do… 🙂

It was meant for a vertex which does not have a parent, in which case the parent value would be zero… It is not needed here as the parent values would come out just fine… I simply used the path printing function in my Bellman Ford algorithm code. The no parent vertex case was a corner case in that scenario… We don’t need it here… If you want to learn more about the other case, I suggest you to go through the comments in my post on Bellman Ford Algorithm.. 🙂